What are the implications of spatial homogeneity in cosmological models?

In summary, spatial homogeneity in a model means that the metric on each invariant hypersurface is described in terms of constants, resulting in a metric that becomes a function of time only. To represent this, one can choose a set of three 1-forms determined by the isometry group, such as dx, dy, dz in Bianchi I, to show the invariance of the metric. However, this choice is not necessary and other bases can be used. The book "Homogeneous Relativistic Cosmologies" by Michael Ryan and Lawrence Shepley is a recommended reference for further understanding.
  • #1
befj0001
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In a spatially homogeneous model, spacetime is filled with a one-parameter set of invariant hypersurfaces H(t). Spatial homogeneity means that the metric on each H(t) is described in terms of constants. Meaning that the metric becomes a function of time only.

I guess that this means that given an isometry group (belonging to the Bianchi classes) one have to choose a set of three 1-forms such that the metric depends on time only? That is, all the Bianchi models can be written in the form where ds^2 is given by:

ds^2 = -dt^2 + g_ij(t)w^ïw^j, where w^i is the set of forms determined by the isometry group such that that the metric becomes a function of t alone.

Could someone clearify this? What do the forms really mean? In Biachi I the forms are given by dx,dy,dz which makes sence. For then the metric will only depend on time since in Biachi I, the isometry group is the group of translations along the spatial coordinate axes.
 
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  • #2
You are free to choose any bases you want in General relativity (as long as they are "good" bases, i.e. linearly independent). You don't have to choose a bases such that g=g(t) alone. You might want to choose such a basis because it manifestly shows the invariance of g, but, for example, if you use spherical coordinates on your hypersurfaces, then g may depend on your spherical coordinates. That's not to say that those symmetries are no longer present, just that they aren't manifest in your coordinate system (Killing's equation is independent of the coordinate system, but is easiest to solve in coordinate systems where g is independent of some coordinates).
 
  • #3
If you haven't already, you should take a look at the book, "Homogeneous Relativistic Cosmologies", by Michael Ryan and Lawrence Shepley. This is the best reference on the subject.
 

FAQ: What are the implications of spatial homogeneity in cosmological models?

What are spatially homogeneous models?

Spatially homogeneous models refer to mathematical or computational models that assume that the distribution of a given variable is uniform or constant across space. This means that the values of the variable do not vary significantly from one location to another.

What are some examples of spatially homogeneous models?

Some examples of spatially homogeneous models include the Poisson distribution, the exponential distribution, and the logistic regression model. These models assume that the underlying variable is equally likely to occur at any point in space.

How are spatially homogeneous models different from spatially heterogeneous models?

Spatially homogeneous models assume that the distribution of a variable is constant across space, while spatially heterogeneous models take into account variations in the distribution of the variable across space. This means that spatially heterogeneous models may be better suited for studying phenomena that exhibit spatial patterns or trends.

What are the advantages of using spatially homogeneous models?

One advantage of using spatially homogeneous models is that they are relatively simple to use and interpret. They also require less data and are less computationally intensive compared to spatially heterogeneous models. This makes them useful for predicting or analyzing variables that do not exhibit significant spatial variations.

What are the limitations of spatially homogeneous models?

One limitation of spatially homogeneous models is that they may not accurately capture the underlying spatial patterns of a variable. This can lead to biased or inaccurate results, especially for variables that exhibit strong spatial variations. Additionally, spatially homogeneous models may not be suitable for studying complex or dynamic systems that involve interactions between different spatial locations.

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